89 research outputs found
Intercropping of Cereals with Lentil: A New Strategy for Producing High-Quality Animal and Human Food
No full textIntercropping is an eco-friendly agricultural practice that can lead to increased productivity and improved resource efficiency. This two-year field study (2022–2023 and 2023–2024) aimed to evaluate the yield and quality (protein content) of lentil when intercropping with bread wheat (Yekora) and oat (Kassandra) under two spatial arrangements (1:1 alternate rows and mixed rows at a 50:50 seeding ratio) in northwestern Greece. A completely randomized design was applied with three replications. Differences were found between treatments regarding yield as well as protein content. Results showed that the highest total grain yield (2478.6 kg/ha) and land equivalent ratio (LER = 2.50) were recorded in the Yekora + Thessalia combination (alternate rows). Legume protein content remained consistently high (27–31%), while cereal protein content varied with genotype. Intercropping in alternate rows generally outperformed mixed sowing, indicating the importance of spatial arrangement in optimizing resource use. These findings suggest that properly designed cereal–lentil intercropping systems can enhance yield and quality while supporting sustainable agricultural practices. Intercropping of Yekora with lentil was superior compared to lentil and bread wheat monocultures and can be recommended as an alternative method for the production of human and animal food
Time-domain, shallow-water hydroelastic analysis of VLFS elastically connected to the seabed
No full textIn order to ensure the safe operation of a VLFS, a combination of mooring, breakwater and other motion reducing systems is employed. In the present work, the transient hydroelastic response of a floating, thin elastic plate, elastically connected to the seabed, is examined. The plate is modelled as an Euler-Bernoulli strip, while the linearized shallow water equations are used for the hydrodynamic modelling. Elastic connectors are approximated by a series of simple spring-dashpot systems positioned along the strip. A higher order finite element scheme is employed for the calculation of the hydroelastic response of the strip-connector configuration, over the shallow bathymetry. After the definition of the initial-boundary value problem, its variational form is derived and discussed. Next, on the basis of the aforementioned formulation, an energy balance expression is obtained. The effect of variable bathymetry on the response of a two connector-strip system, is examined by means of three seabed profiles, featuring a flat bottom, an upslope and a downslope environment. For the flat bottom case, the strip response mitigating effect exerted by the employment of two and three elastic connectors is considered. Finally, by means of the derived energy balance equation, the energy exchange is monitored, providing a valuable insight into the transient phenomena that take place in the studied configurations.The present work has been supported by the project HYDELFS funded by the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF 2007-2013) -Research Funding Program ARHIMEDES-III: Investing in knowledge society through the European Social Fund. In particular, author Theodosios K. Papathanasiou acknowledges support from the aforementioned program for the period from 6/9/2012 to 30/09/2014
Equivalent conditions on the central limit theorem for a sequence of probability measures on R
No full textIn this paper we give equivalent conditions on the central limit theorem in total variation norm for a sequence of probability measures on R. This generalizes Cacoullos, Papathanasiou and Utev's central limit theorem in L1-norm for a sequence of probability density functions on R. We also give equivalent conditions on the central limit theorem in weak convergence and those on the local limit theorem
Bayesian Non-Parametric Thermal Thresholds for Helicoverpa armigera and Their Integration into a Digital Plant Protection System
No full textThe development of temperature-driven pest risk thresholds is a prerequisite for the buildup and implementation of smart plant protection solutions. However, the challenge is to convert short and abrupt phenology data with limited distributional information into ecological relevant information. In this work, we present a novel approach to analyze phenology data based on non-parametric Bayesian methods and develop degree-day (DD) risk thresholds for Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae) to be used in a decision support system for dry bean (Phaseolus vulgaris L.) production. The replication of each Bayesian bootstrap generates a posterior probability for each sampling set by considering that the prior unknown distribution of pest phenology is Dirichlet distribution. We computed R = 10,000 temperature-driven pest phenology replicates, to estimate the 2.5%, 50% and 95.5% percentiles (PC) of each flight generation peak in terms of heat summations. The related DD thresholds were: 114.04 (PC 2.5%) 131.8 (PC 50%) and 150.9 (PC 95.5%) for the first, 525.8 (PC 2.5%), 551.7 (PC 50%) and 577.6 (PC 95.5%) for the second and 992.7 (PC 2.5%), 1021.5 (PC 50%) and 1050 (PC 95.5%) for the third flight, respectively. The thresholds were evaluated by estimating the posterior differences between the predicted (2021) and observed (2022) phenology metrics and are in most cases in acceptable levels. The bootstrapped Bayesian risk thresholds have the advantage to be used in modeling short and noisy data sets providing tailored pest forecast without any parametric assumptions. In a second step the above thresholds were integrated to a sub-module of a digital weather-driven real time decision support system for precise pest management for dry bean crops. The system consists of a customized cloud based telemetric meteorological network, established over the border area of the Prespa National Park in Northern Greece, and delivers real time data and pest specific forecast to the end user
Predicting the Occurrence and Risk Damage Caused by the Two-Spotted Spider Mite <i>Tetranychus urticae</i> (Koch) in Dry Beans (<i>Phaseolus vulgaris</i> L.) Combining Rate and Heat Summation Models for Digital Decisions Support
No full textIn this work, we use developmental rate models to predict egg laying activity and succession of generations of T. urticae populations under field conditions in the Prespa lakes region in Northern Greece. Moreover, the developmental rate model predictions are related to accumulated heat summations to be compared with actual bean damage and to generate pest-specific degree-day risk thresholds. The oviposition was predicted to start at 57.7 DD, while the first peak in egg laying was estimated to be at 141.8 DD. The second and third peak in egg production were predicted to occur at 321.1 and 470.5 DD, respectively. At the degree-day risk threshold, half development of the first summer generation was estimated at 187 DD and 234 DDm while for the second, it was estimated at 505 DD and 547 DD for 2021 and 2022, respectively. According to the model predictions, no significant differences were observed in the mean generation time (total egg to adult development) of T. urticae between the two observation years (t = 0.01, df = 15, p = 0.992). The total generation time was estimated at 249.3 (±7.7) and 249.2 (±6.7), for 2021 and 2022, respectively. The current models will contribute towards predictions of the seasonal occurrence and oviposition of T. urticae to be used in pest management decision-making. Moreover, the development of population model is a prerequisite for the buildup and implementation of smart plant protection solutions
Smart Window Integration into the Building Envelope: Implementation of a smart window integrated by luminescent solar concentrators into the building envelope– large-scale application: main facade of Civil Engineering building of TU Delft
No full textIn the present research the building performance by the incorporation of a Smart Window integrated by LSC components into the building envelope is investigated in terms of daylight and energy consumption. From energy point of view, the main objective of the Smart Window was to provide ''usable'' daylight to the interior of the offices, allowing the electric lighting to be replaced by natural light and reducing the heating and cooling loads. At the same time, the proposed daylight system aimed to increase visual comfort levels, ensuring a productive and enjoyable working environment. For evaluation purposes, daylight and energy simulations were performed for different configurations and modulations of the Smart Window. The software packages of Radiance Desktop and Ecotect were used for the determination of the daylight-related performance indicators of illuminance, uniformity index and daylight factor while the energy-related ones, heating, cooling and lighting demand, as well as discomfort glare, were identified by the advanced software of Energy Plus. Two case study buildings were analyzed under two dominant daylight conditions (overcast and clear sky condition). By the application of the Smart Window firstly on an office room of the South facade of ENI Donegani Institute in Novara, Italy, and secondly on a South-west office room of the Civil Engineering main building at the campus of TU Delft, Netherlands, useful conclusions were drawn. The results of the first performance analysis of the smart window showed advantages such as better control of solar radiation, higher uniformity and light distribution but also some drawbacks. The energy performance of the building, integrated by the smart window into its envelope, is strongly dependent on the type of energy that is used to cover heating and cooling demand.Building Engineering/Building Technology and PhysicsStructural EngineeringCivil Engineering and Geoscience
Reliability Forecasting for Simulation-based Workforce Planning
No full textThe problem owner of the present study is a consulting company that provides simulation-based workforce planning advice to a big manufacturing firm XYZ. The latter pertains the number of engineers of various skill levels that are needed for the repair of health care equipment in hospitals of a large country. The prediction of machine failures (reliability forecasting) is a crucial input to the simulations that affects the quality of the business advice. Currently, the problem owner follows a reliability forecasting approach based on lifetime models following the HPP [1]. Nevertheless, this practice has several limitations as: i) the predictive performance is not always satisfactory due to data overfitting (Liang, 2011), ii) real-world systems do not generally comply with the HPP traits (Kurien, Sekhon & Chawla, 1993), namely constant failure rates of a memoryless failure process, while reliability is non-linear and complex due to a bunch of factors (Chatterjee & Bandopadhyay, 2012). In the view of the above, the problem owner needs to increase the efficiency of workforce planning that will finally lead to cost savings for firm XYZ. It is believed that a more efficient planning can be achieved through the improvement of the forecasting approach. Forecasting should fulfil certain requirements, namely it should predict the failure patterns of multiple machines, at an acceptable level of accuracy, with a high degree of automation. Thus, the study’s research objective is defined as: to provide an automated forecasting framework that detects and predicts the failure patterns of multiple machines with acceptable accuracy. For achieving the research objective, firstly, a clarification of the forecasting requirements is done through a semi-structured interview with the problem owner. Among others, it is clarified that accuracy is the hourly absolute deviation between the actual and the forecasted inter-failure time of a machine (MAE), and it concerns only its next failure (one-step ahead forecasting). Additionally, for a bunch of reasons, two different levels of acceptable accuracy are defined, the 1st with minimum accuracy of 120h (1 working week), and the 2nd of 2160h (1 quarter). Secondly, the identification of the most promising forecasting approach that can fulfil the given requirements is done through a literature review. Time series forecasting is found to be the most promising approach as it: i) outperforms reliability models that follow the NHPP in terms of accuracy (Ho & Xie, 1998; Dindarloo & Siami-Irdermoosa, 2015; Fan & Fan, 2015), ii) is able for automated and large-scale application (Wagner et al., 2011). Subsequently, a case study, which pertains reliability forecasting of radiation treatment machines maintained by firm XYZ, is conducted in order to evaluate the time series approach. The reliability metric of Time-Between-Failures (TBF) is used for forecasting, whilst the time series cross-validation method is employed for its evaluation. The time series approach followed is based on the use of three parametric methods (ARIMA, Exponential Smoothing, Optimized Theta) and two artificial neural networks (FFNN, RGMDH) applied on the machine group level (2 groups) and on the individual machine level (5 machines). In this context, experimentations take place under both full and adjusted for outliers data conditions. Moreover, the related repair data, expressed by Time-To-Repair (TTR) and by a dummy variable that represents the use of spare items, is used in the TBF forecasting with ARIMAX models. The case study demonstrates that: i) on the machine group level, the series are white noise involving that the failure process is memoryless and failure patterns cannot be detected, ii) on the individual machine level, the best performing forecast model of every machine examined satisfies the 2nd level of acceptable accuracy. The MAE error metric of the best forecast model for each machine examined is substantially less than 2160h. Thus, the present study has succeeded in its objective. The reliability forecasting framework produced constitutes a holistic approach to the prediction of machine failures, as with its various and at a degree, complementary methods can deal with all the basic types of failure data (e.g. autocorrelations, seasonality, trend, non/linearity, etc.) The framework formed is provided to the problem owner allowing for the transformation of the workforce planning of firm XYZ from an annual to a quarterly basis. The recommendations for the problem owner as well as for future research are: first, the execution of experimental simulations with a planning horizon of 3 months in order to evaluate the possible cost savings for firm XYZ. Second, the collection of new relevant to machine failures data (e.g. machine utilization, purchase date), and third, the extension and evaluation of the forecasting framework with the inclusion of these new data and/or with new methods (e.g. hybrid, FFNN with external covariates) and techniques (e.g. time series clustering). Fourth, the application and re-evaluation of the reliability forecasting framework formed when the failure data of 2016 become available. Fifth, the use of failure behavior’s variability as a stakeholder management tool when the problem owner deals with forecasting projects. Last, the use of the time series cross-validation method for the evaluation of forecast models and the great amount of attention on the potential existence of outliers in the dataset. On reflection, the contribution of the present thesis is multi-dimensional. First, a holistic and multi-method reliability forecasting framework that can deal with almost any failure process has been produced. This framework can be used in relevant projects as it can be extended and adjusted to the conditions of each project. Second, the aforementioned framework has been built though a state-of-the-art analytical forecasting process that can also be used by the problem owner in different projects. Third, there is a clear potential for cost savings for firm XYZ if workforce planning is adjusted in a quarterly horizon. Fourth, there is a knowledge contribution to the performance of various time series methods (e.g. Optimized Theta, RGMDH) in the context of reliability forecasting. Fifth, there is a clear contribution to the increase of the domain knowledge of reliability forecasting in health care equipment in general, and in radiation treatment machines in particular. Last, it has been highlighted that the initial evaluation of the variability of the failure behavior of a set of machines can serve as a stakeholder management tool as regards the final forecasting deliverable.Technology, Policy and ManagementMulti Actor SystemsM.Sc. in Engineering and Policy Analysis (EPA
Important Parameters Connected to Farmers’ Networking and Training That Give Added Value to “Fasolia Vanilies Feneou” and “Fava Feneou” Products
No full textThe official designation of the bean “Fasolia Vanilies Feneou” and grass pea “Fava Feneou” as Protected Geographical Indication (PGI) products do not extend protection to their cultivated genetic material due to their non-inclusion in the National Catalog of Varieties [EC 2008/62/EK (official Greek Gazette) FEK 165/30-Juanuary-2014] as recognized traditional cultivars. This omission poses a significant risk to the genetic diversity of these varieties, potentially leading to the loss of their distinct characteristics, decreased yields, and compromised quality. The primary objective of this project is to ensure the preservation of these local varieties through a comprehensive study of their genetic variability. Additionally, it aims to adhere to official protocols for describing and subsequently registering these varieties in the National List of Varieties. This registration will enhance the product’s value and secure its unique identity. The experimentation phase of the project focuses on evaluating the landrace to select plants that demonstrate improved productivity and quality. This work presents the parameters connected with the description of the unique identity of this product; its origin, traceability, and local agricultural practices; and specific product characteristics that will contribute to this. The product will be utilized by Kiato Union IKE and, at the same time, farmers will be trained in the excellent seed reproduction and production of the product. This initiative promises several benefits for the agricultural cooperative and producers in Feneos
Lean Rules Identification and Classification for Manufacturing Industry
No full textAbstractLean theory, principles and tools, are intended to highlight the value within the company and eliminate waste entirely. Despite the large amount of literature work on lean, there is a lack of in-depth analysis for collection and categorization of specific lean rules for the manufacturing industry. Therefore, the present work proposes a classification, formalization and identification of lean rules, in order to create a comprehensive and applicable library of lean rules, after the investigation of academic literature and a mould-making industry. Finally, the “drawer” idea is introduced aiming to motivate employees of different hierarchical levels in the lean rules development
A closed-form solution for the barrier properties of randomly oriented high aspect ratio flake composites
No full textWe derive closed-form solutions for the barrier factor of flake-filled composites, in which the flakes are randomly placed and oriented, with the orientation angles uniformly distributed in an interval [−ɛ, +ɛ], 0 1000) in which (αϕ) can reach levels in excess of 100. Comparison of the derived closed-form solutions to computational results reveals that both the harmonic and the arithmetic averages are adequate in dilute systems; however, at large values of (αϕ) and (ɛ), only the harmonic average is in good agreement with the computational results. The predictions of the proposed solution are also compared to those of existing literature models for the effective diffusivity of flake composites. We find that discrepancies become very significant at large (ɛ) and also at large values of (αϕ), pointing further to the conclusion that the proposed solution is currently the only accurate one to predict the effective diffusivity of randomly oriented and highly concentrated nano-flake composites. © The Author(s) 2019
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